Solving Traffic Signal Setting Problem Using Machine Learning

Góra, Paweł; Brzeski, Maciej; Karnas, Katarzyna; Przybyszewski, Przemyslaw; Klemenko, Arkadiusz; Kochański, Adrian; Dryja, Hubert; Kukawska, Magdalena; Kopczyk, Dawid; Możejko, Marcin

doi:10.1109/mtits.2019.8883380

Cited by 3 publications

(1 citation statement)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, heuristic-based studies in the past have mostly dealt with signal control optimization problems from the perspectives of mono-objective or bi-objective solutions without catering for multiple objectives simultaneously. Some popular heuristic techniques used for intelligent traffic control through signalized intersections include genetic algorithms (GA), ant colony optimization (ACO), differential evolution (DE), artificial immune system (AIS), reinforcement learning (RL), and different machine learning algorithms [57][58][59][60][61][62][63].…”

Section: Related Workmentioning

confidence: 99%

On the Potential Impacts of Smart Traffic Control for Delay, Fuel Energy Consumption, and Emissions: An NSGA-II-Based Optimization Case Study from Dhahran, Saudi Arabia

et al. 2020

View full text Add to dashboard Cite

Intelligent traffic control at urban intersections is vital to ensure efficient and sustainable traffic operations. Urban road intersections are hotspots of congestion and traffic accidents. Poor traffic management at these locations could cause numerous issues, such as longer travel time, low travel speed, long vehicle queues, delays, increased fuel consumption, and environmental emissions, and so forth. Previous studies have shown that the mentioned traffic performance measures or measures of effectiveness (MOEs) could be significantly improved by adopting intelligent traffic control protocols. The majority of studies in this regard have focused on mono or bi-objective optimization with homogenous and lane-based traffic conditions. However, decision-makers often have to deal with multiple conflicting objectives to find an optimal solution under heterogeneous stochastic traffic conditions. Therefore, it is essential to determine the optimum decision plan that offers the least conflict among several objectives. Hence, the current study aimed to develop a multi-objective intelligent traffic control protocol based on the non-dominated sorting genetic algorithm II (NSGA-II) at isolated signalized intersections in the city of Dhahran, Kingdom of Saudi Arabia. The MOEs (optimization objectives) that were considered included average vehicle delay, the total number of vehicle stops, average fuel consumption, and vehicular emissions. NSGA-II simulations were run with different initial populations. The study results showed that the proposed method was effective in optimizing considered performance measures along the optimal Pareto front. MOEs were improved in the range of 16% to 23% compared to existing conditions. To assess the efficacy of the proposed approach, an optimization analysis was performed using a Synchro traffic light simulation and optimization tool. Although the Synchro optimization resulted in a relatively lower signal timing plan than NSGA-II, the proposed algorithm outperformed the Synchro optimization results in terms of percentage reduction in MOE values.

show abstract

Section: Related Workmentioning

confidence: 99%

On the Potential Impacts of Smart Traffic Control for Delay, Fuel Energy Consumption, and Emissions: An NSGA-II-Based Optimization Case Study from Dhahran, Saudi Arabia

et al. 2020

View full text Add to dashboard Cite

show abstract

An adversarial diverse deep ensemble approach for surrogate‐based traffic signal optimization

Tang,

Wang,

Chung

et al. 2024

Computer aided Civil Eng

View full text Add to dashboard Cite

Surrogate‐based traffic signal optimization (TSO) is a computationally efficient alternative to simulation‐based TSO. By replacing the simulation‐based objective function, a surrogate model can quickly identify solutions by searching for extreme points on its response surface. As a popular surrogate model, the ensemble of multiple diverse deep learning models can approximate complicated systems with a strong generalizability. However, existing ensemble methods barely focus on strengthening the prediction of extreme points, which we found can be realized by further diversifying base learners in the ensemble. The study proposes an adversarial diverse ensemble (ADE) method for online TSO with limited computational resources, comprising two stages: In the offline stage, base extractors are diversified with unlabeled data by a designed adversarial diversity training algorithm; in the online stage, base predictors are trained in parallel with limited labeled data, and the ensemble then serves as the surrogate model to search for solutions iteratively for TSO. First, it is demonstrated that the prediction accuracy on extreme points, and associated solution quality, can be constantly improved with base learners’ diversity enhanced by ADE. Case studies of TSO conducted on a four‐intersection arterial further demonstrate the superior solution quality and computational efficiency of the ADE surrogate model in a wide range of traffic scenarios. Moreover, a large‐scale online TSO experiment under dynamic traffic demand proves ADE's effectiveness in practical applications.

show abstract

AI-Enabled Spatial Intelligence

Ahmad

2023

Advances in Linguistics and Communication Studies

View full text Add to dashboard Cite

GIS plays a crucial role in helping decision-makers to analyze and interpret complex spatial data to make informed decisions. However, conventional GIS workflows are inadequate for managing the massive amounts of spatial data that are necessary for decision-making. This chapter discussed the transformative potential of AI and spatial data in enhancing spatial intelligence. It first outlined the challenges associated with GIS, specifically related to data management and decision-making. These challenges are categorized into technical, data, and human-centered. Next, the chapter presented AI-based strategies that can be used to address these challenges, along with their potential impact. The chapter also examined the ethical considerations surrounding AI-powered GIS and suggested approaches to address them. This chapter can serve as a valuable source of information for researchers, practitioners, and policymakers operating in the realm of AI-based GIS.

show abstract

Solving Traffic Signal Setting Problem Using Machine Learning

Cited by 3 publications

References 14 publications

On the Potential Impacts of Smart Traffic Control for Delay, Fuel Energy Consumption, and Emissions: An NSGA-II-Based Optimization Case Study from Dhahran, Saudi Arabia

On the Potential Impacts of Smart Traffic Control for Delay, Fuel Energy Consumption, and Emissions: An NSGA-II-Based Optimization Case Study from Dhahran, Saudi Arabia

An adversarial diverse deep ensemble approach for surrogate‐based traffic signal optimization

AI-Enabled Spatial Intelligence

Contact Info

Product

Resources

About